This study developed a methodology to temporally classify
large scale, upper level atmospheric conditions over North America,
utilizing a newly-developed upper level synoptic classification
(ULSC). Four meteorological variables: geopotential height, specific
humidity, and u- and v-wind components, at the 500 hPa level
over North America were obtained from the NCEP/NCAR Reanalysis
Project dataset for the period 1965-1974. These data were subjected
to principal components analysis to standardize and reduce the
dataset, and then an average linkage clustering algorithm identified
groups of observations with similar flow patterns. The procedure
yielded 16 clusters. These flow patterns identified by the ULSC
typify all patterns expected to be observed over the study area.
Additionally, the resulting cluster calendar for the period 1965-1974
showed that the clusters are generally temporally continuous.
Subsequent classification of additional observations through
a z-score method produced acceptable results, indicating that
additional observations may easily be incorporated into the ULSC
calendar. The ULSC calendar of synoptic conditions can be used
to identify situations that lead to periods of extreme weather,
i.e., heat waves, flooding and droughts, and to explore long-distance
dispersal of airborne particles and biota across North America.